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Spectral Clustering is Just K-Means on Eigenvectors

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Spectral Clustering is Just K-Means on Eigenvectors

Spectral clustering isn't a new algorithm, it's plain k-means in disguise. K-means slices straight through two concentric rings and fails, because it only sees distance to a center. So spectral clustering changes where the points live first: connect each point to its neighbors, build the graph Laplacian (degree minus weights), and read off its ...

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